Many optical systems are used to detect the position of a member or an object for the purposes of inputting data, in particular alphanumeric data.
The patent FR 2 443 173 describes a keypad with movable keys including a plurality of light emitters and a plurality of light receivers for detecting depression of the keys; that keypad is costly because it requires a large number of mechanical and optoelectronic components and it is complex because light emitters and receivers are distributed throughout the area in which the keys are situated.
More recently, static data input devices have been developed.
U.S. Pat. No. 4,986,662 describes a system similar to the above system in which the sources are placed at the focuses of respective parabolic reflectors in order to reduce the number of optoelectronic components.
One drawback of the above devices is that it is necessary to distribute the optical components over substantially the whole of the perimeter of the input area with sufficient geometrical accuracy not to falsify calculations based on data representative of signals delivered by the light receivers to determine the position in the input area of the object (finger, etc.) used to indicate data selected by the user. As a result of this, such devices remain relatively costly.
Furthermore, a device designed for a surface of given size is not easily adaptable to a surface of different size.
Document EP 1 039 365 describes a virtual keypad including infrared light transducers adapted to determine the distance of an object (finger, stylus) by measuring the attenuation of light reflected by that object.
To be more precise, that method is based on taking the signal received by the transducer delivering the greatest amount of energy and processing it in accordance with an inverse square law based on the distance between the object and the receiver (and the lower energy signals received by the other transducers are not processed).
Those values are divided into ranges each corresponding to one row of the virtual keypad and then sampled.
That method has a first drawback in that it is not able to detect the position of an object accurately if it is not perfectly aligned with the infrared receiver.
It has a second drawback in that it is not able to obtain the precise position of an object of irregular shape (for example a finger) even if the said object is aligned with a detector, in particular because the signal received after reflection from an irregular shape is far from linear.
Naturally, one solution to that problem is to increase the number of transducers, to the detriment of the cost and the complexity of the device.
Document EP 1 168 233 describes an optical detection device using a neural network to analyze the reflected signal.
One drawback of that method is that it requires a large number of samples to train the neural network.
If the neural network is trained with a relatively small number of samples (relative to the number of neurons), the result of the interpolation effected by the neural network is reliable only for detecting objects under the same conditions as apply to the above-mentioned samples, and it becomes difficult to predict and very uncertain as soon as the measurement conditions depart from the training conditions.
Over and above this, the execution of a neural network in an embedded system consumes a detrimental quantity of computation resources.